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The suitability of spotlight counts to index red fox abundance was assessed in an arid environment through a comparison with a scat deposition index (active attractant). In most cases there was a high degree of correlation between the two indices, suggesting that the spotlight counts were accurately documenting fluctuations in population size. However, the precision of the spotlight index was often low (c.v. = 0.07–0.46), suggesting that the technique may not allow the statistical detection of small changes in abundance. During periods when there was an influx of new individuals into the population, the seasonal scat index displayed a three-month time lag in documenting abundance while foxes accustomed themselves to the presence of the regular food supply. The level of precision of the scat index was also found to be relatively low (c.v. = 0.21–0.48). Nevertheless, further refinements of this technique may produce a suitable measure of fox abundance.

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Currently, online organizational resources and assets are potential targets of several types of attack, the most common being flooding attacks. We consider the Distributed Denial of Service (DDoS) as the most dangerous type of flooding attack that could target those resources. The DDoS attack consumes network available resources such as bandwidth, processing power, and memory, thereby limiting or withholding accessibility to users. The Flash Crowd (FC) is quite similar to the DDoS attack whereby many legitimate users concurrently access a particular service, the number of which results in the denial of service. Researchers have proposed many different models to eliminate the risk of DDoS attacks, but only few efforts have been made to differentiate it from FC flooding as FC flooding also causes the denial of service and usually misleads the detection of the DDoS attacks. In this paper, an adaptive agent-based model, known as an Adaptive Protection of Flooding Attacks (APFA) model, is proposed to protect the Network Application Layer (NAL) against DDoS flooding attacks and FC flooding traffics. The APFA model, with the aid of an adaptive analyst agent, distinguishes between DDoS and FC abnormal traffics. It then separates DDoS botnet from Demons and Zombies to apply suitable attack handling methodology. There are three parameters on which the agent relies, normal traffic intensity, traffic attack behavior, and IP address history log, to decide on the operation of two traffic filters. We test and evaluate the APFA model via a simulation system using CIDDS as a standard dataset. The model successfully adapts to the simulated attack scenarios’ changes and determines 303,024 request conditions for the tested 135,583 IP addresses. It achieves an accuracy of 0.9964, a precision of 0.9962, and a sensitivity of 0.9996, and outperforms three tested similar models. In addition, the APFA model contributes to identifying and handling the actual trigger of DDoS attack and differentiates it from FC flooding, which is rarely implemented in one model.

Abstract-This paper presents the major findings from a study conducted with six different universities in the U.S. regarding their use of the Learning Analytics (LA) capabilities available within their learning management systems (LMS). Data was collected from an online survey instrument, in-depth interviews with IT directors and academic administrators, and a case study in Embry-Riddle Aeronautical University. One observation is that universities are attempting to make better use of new analytics functions and the data stored in the university LMS in order to make more informed decisions regarding short-term and longterm goals and objectives. The new functions include analytics performed at the institutional level, college level, degreeprogram level, course level, and even course section level. Courses and degree programs as well as learning performance and objectives can be measured and analyzed using different goals, criteria, and accreditation requirements.